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                        <span class="time">10月</span>
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                        <span class="count">11篇</span>
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                        <span class="count">7篇</span>
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                        <span class="count">4篇</span>
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                        <span class="time">11月</span>
                        <span class="count">5篇</span>
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                        <a href="https://blog.csdn.net/bitcarmanlee/article/month/2018/10" target="_blank" data-report-click='{"mod":"popu_538","ab":"new","dest":""}' data-report-view='{"mod":"popu_538","ab":"new","dest":""}'>
                        <span class="time">10月</span>
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                        <span class="time">9月</span>
                        <span class="count">12篇</span>
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                        <span class="count">7篇</span>
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                        <span class="time">7月</span>
                        <span class="count">2篇</span>
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                        <span class="time">6月</span>
                        <span class="count">7篇</span>
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                        <span class="time">5月</span>
                        <span class="count">4篇</span>
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                        <span class="time">4月</span>
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                <h1 class="title-article">Apache Parquet 与Apache ORC简介</h1>
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                                                                                                                                            <a class="follow-nickName" href="https://me.csdn.net/bitcarmanlee" target="_blank" rel="noopener">bitcarmanlee</a>
                    <span class="time">2016-07-23 20:08:19</span>
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                                <div class="up-time"><span>最后发布:2016-07-23 20:08:19</span><span>首发:2016-07-23 20:08:19</span></div>
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                                            <h2 id="1列存储的优势">1.列存储的优势</h2>

<p>传统的 RDBMS 大多使用的行存储方式，现如今随着大数据技术的发展，对于存储的要求越来越高，列存储相对有自己明显的优势：列数据相对来说比较类似，压缩比更高；一般的查询只涉及几列，列存储的查询性能也更高；可以方便地新增列等。在某些场景下，选择列存储是非常不错的选择，从节省存储的角度来说就非常吸引人了。</p>

<p>目前，比较有名的开源实现有 Apache Parquet 和 Apache ORC。ORC 官网介绍 Facebook 和 Yahoo 都使用了 ORC。</p>

<h2 id="2parquet">2.Parquet</h2>

<p>pache Parquet 是 Apache 的顶级项目，是一个自描述语言无关的列存储，最初的设计动机是为了存储嵌套数据，可以将 ProtoBuf、Thrift、Avro 等格式的数据转换为列格式来存储，这是其最大的优势。此外，也有基本的功能，数据压缩、基本的索引功能、支持复杂类型等。主要在 Apache Drill 中使用。</p>

<p>从 Hive 0.13 开始，Hive 开始原生支持 Parquet，注意不是全部类型都支持，详情请参考 Hive 官方文档。使用方法很简单，只需要在建表语句的时候，声明存储格式即可。</p>

<pre class="prettyprint"><code class=" hljs sql"><span class="hljs-comment">-- 建表，通过修改 parquet.compression 参数来修改压缩算法</span>
<span class="hljs-operator"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> parquet_test (
 id <span class="hljs-keyword">int</span>,
 str string,
 mp MAP&lt;STRING,STRING&gt;,
 lst ARRAY&lt;STRING&gt;,
 strct STRUCT&lt;A:STRING,B:STRING&gt;)
PARTITIONED <span class="hljs-keyword">BY</span> (part string)
STORED <span class="hljs-keyword">AS</span> PARQUET TBLPROPERTIES (<span class="hljs-string">"parquet.compression"</span>=<span class="hljs-string">"SNAPPY"</span>);</span>

<span class="hljs-comment">-- 数据迁移只需要将原来的表数据重新插入一遍即可</span>
<span class="hljs-operator"><span class="hljs-keyword">INSERT</span> OVERWRITE <span class="hljs-keyword">TABLE</span> parquet_test <span class="hljs-keyword">SELECT</span> * <span class="hljs-keyword">FROM</span> old_test;</span></code></pre>

<p>数据存储格式图 <br>
<img src="https://img-blog.csdn.net/20160723200538240" alt="这里写图片描述" title=""></p>

<h2 id="3orc">3.ORC</h2>

<p>ORC（OptimizedRC File）也是 Apache 的顶级项目，也是自描述的列存储，源自于RC（RecordColumnar File），主要是在 Hive 中使用，支持数据压缩、索引功能、update 操作、ACID 操作、支持复杂类型等，其查询性能相对 Parquet 略有优势。</p>

<p>从 Hive 0.11 版本开始引入，使用方法也很简单。</p>



<pre class="prettyprint"><code class=" hljs r">CREATE TABLE ORC_test (
  <span class="hljs-keyword">...</span>
) STORED AS ORC TBLPROPERTIES (<span class="hljs-string">"orc.compress"</span>=<span class="hljs-string">"NONE"</span>);

-- merge small ORC files into a larger file, starting <span class="hljs-keyword">in</span> hive <span class="hljs-number">0.14</span>
ALTER TABLE ORC_test PARTITION(<span class="hljs-keyword">...</span>) CONCATENATE;</code></pre>

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